Lila Sciences, Inc.
ML Scientist - Materials Performance Modeling
Lila Sciences, Inc., Cambridge, Massachusetts, us, 02140
Overview
ML Scientist - Materials Performance Modeling | Cambridge, MA Lila Sciences is the worlds first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science. We are pioneering a new age of boundless discovery by applying AI to every aspect of the scientific method. Learn more about this mission at www.lila.ai. If this sounds like an environment youd love to work in, even if you only have some of the experience listed below, we encourage you to apply. Responsibilities
Develop ML models to predict
materials performance and reliability
under diverse application conditions (e.g., stress, temperature, chemical environments, aging). Design
data-efficient learning strategies
for sparse, small, or incomplete experimental datasets. Integrate
physics-informed priors, time-series prediction concepts, multi-modal methods and probabilistic modelling
into predictive frameworks. Collaborate with materials scientists to
curate, preprocess, and interpret
complex experimental and simulation data. Build scalable ML workflows that can be deployed within Lilas platforms. Qualifications
PhD (preferred) or equivalent experience in
Materials Science, Applied Physics, Machine Learning, Computer Science or related fields . Strong proficiency in
Python
and modern ML frameworks (PyTorch, TensorFlow, JAX) and models in sparse, time-dependent data settings (few-shot learning, time-series prediction). Familiarity with
materials datasets
(experimental and/or computational) and performance characterization. Ability to collaborate across ML and materials science teams to deliver impactful methods and frameworks. Experience with
time dependent data modeling
methods. Experience with
physics-informed ML
or
hybrid physics/ML approaches . Familiarity with
multimodal data integration
(e.g., combining simulation, imaging, spectroscopy, and tabular data). Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. Notes
A Note to Agencies Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Sciences internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto. #J-18808-Ljbffr
ML Scientist - Materials Performance Modeling | Cambridge, MA Lila Sciences is the worlds first scientific superintelligence platform and autonomous lab for life, chemistry, and materials science. We are pioneering a new age of boundless discovery by applying AI to every aspect of the scientific method. Learn more about this mission at www.lila.ai. If this sounds like an environment youd love to work in, even if you only have some of the experience listed below, we encourage you to apply. Responsibilities
Develop ML models to predict
materials performance and reliability
under diverse application conditions (e.g., stress, temperature, chemical environments, aging). Design
data-efficient learning strategies
for sparse, small, or incomplete experimental datasets. Integrate
physics-informed priors, time-series prediction concepts, multi-modal methods and probabilistic modelling
into predictive frameworks. Collaborate with materials scientists to
curate, preprocess, and interpret
complex experimental and simulation data. Build scalable ML workflows that can be deployed within Lilas platforms. Qualifications
PhD (preferred) or equivalent experience in
Materials Science, Applied Physics, Machine Learning, Computer Science or related fields . Strong proficiency in
Python
and modern ML frameworks (PyTorch, TensorFlow, JAX) and models in sparse, time-dependent data settings (few-shot learning, time-series prediction). Familiarity with
materials datasets
(experimental and/or computational) and performance characterization. Ability to collaborate across ML and materials science teams to deliver impactful methods and frameworks. Experience with
time dependent data modeling
methods. Experience with
physics-informed ML
or
hybrid physics/ML approaches . Familiarity with
multimodal data integration
(e.g., combining simulation, imaging, spectroscopy, and tabular data). Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. Notes
A Note to Agencies Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Sciences internal Talent Acquisition team. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto. #J-18808-Ljbffr